Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
This work addresses the need for reliable uncertainty quantification in drug discovery, where imprecise predictions waste resources, but it is incremental as it compares existing methods without proposing new ones.
The paper systematically evaluated uncertainty quantification methods for neural networks in molecular property prediction, finding that no method was unequivocally superior or reliably ranked errors across five benchmark datasets.
Uncertainty quantification (UQ) is an important component of molecular property prediction, particularly for drug discovery applications where model predictions direct experimental design and where unanticipated imprecision wastes valuable time and resources. The need for UQ is especially acute for neural models, which are becoming increasingly standard yet are challenging to interpret. While several approaches to UQ have been proposed in the literature, there is no clear consensus on the comparative performance of these models. In this paper, we study this question in the context of regression tasks. We systematically evaluate several methods on five benchmark datasets using multiple complementary performance metrics. Our experiments show that none of the methods we tested is unequivocally superior to all others, and none produces a particularly reliable ranking of errors across multiple datasets. While we believe these results show that existing UQ methods are not sufficient for all common use-cases and demonstrate the benefits of further research, we conclude with a practical recommendation as to which existing techniques seem to perform well relative to others.